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Generalized method of moment and indirect estimation of the ARasMA model
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
1998 (English)In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 13, no 4, 485-494 p.Article in journal (Refereed) Published
Abstract [en]

Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both approaches are easy to implement and perform well numerically. In a Monte Carlo study it is found that the MSE properties of GMM are close to those of ML. The indirect estimator performs poorly in this respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test.

Place, publisher, year, edition, pages
1998. Vol. 13, no 4, 485-494 p.
Keyword [en]
Estimation, Nonlinearity test, Small sample properties, Time series
National Category
Economics
Research subject
Econometrics
Identifiers
URN: urn:nbn:se:umu:diva-64766OAI: oai:DiVA.org:umu-64766DiVA: diva2:602689
Available from: 2013-02-02 Created: 2013-02-02 Last updated: 2017-12-06Bibliographically approved

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Brännäs, KurtDe Luna, Xavier

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